Integrating omic approaches for abiotic stress tolerance in soybean

REVIEW ARTICLEpublished: 03 June 2014

doi: 10.3389/fpls.2014.00244

Integrating omic approaches for abiotic stress tolerance insoybeanRupesh Deshmukh , Humira Sonah , Gunvant Patil , Wei Chen , Silvas Prince , Raymond Mutava ,

Tri Vuong , Babu Valliyodan and Henry T. Nguyen*

National Center for Soybean Biotechnology and Division of Plant Sciences, University of Missouri, Columbia, MO, USA

Edited by:

Rajeev K. Varshney, InternationalCrops Research Institute for theSemi-Arid Tropics, India

Reviewed by:

Paula Casati, Centro de EstudiosFotosinteticos-CONICET, ArgentinaIain Robert Searle, The University ofAdelaide, Australia

*Correspondence:

Henry T. Nguyen, National Center forSoybean Biotechnology and Divisionof Plant Sciences, University ofMissouri, 1-31 Agriculture Building,Columbia, MO 65211-7140, USAe-mail: [email protected]

Soybean production is greatly influenced by abiotic stresses imposed by environmentalfactors such as drought, water submergence, salt, and heavy metals. A thoroughunderstanding of plant response to abiotic stress at the molecular level is a prerequisite forits effective management. The molecular mechanism of stress tolerance is complex andrequires information at the omic level to understand it effectively. In this regard, enormousprogress has been made in the omics field in the areas of genomics, transcriptomics,and proteomics. The emerging field of ionomics is also being employed for investigatingabiotic stress tolerance in soybean. Omic approaches generate a huge amount of data,and adequate advancements in computational tools have been achieved for effectiveanalysis. However, the integration of omic-scale information to address complex geneticsand physiological questions is still a challenge. In this review, we have describedadvances in omic tools in the view of conventional and modern approaches being usedto dissect abiotic stress tolerance in soybean. Emphasis was given to approaches suchas quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), andgenomic selection (GS). Comparative genomics and candidate gene approaches are alsodiscussed considering identification of potential genomic loci, genes, and biochemicalpathways involved in stress tolerance mechanism in soybean. This review also provides acomprehensive catalog of available online omic resources for soybean and its effectiveutilization. We have also addressed the significance of phenomics in the integratedapproaches and recognized high-throughput multi-dimensional phenotyping as a majorlimiting factor for the improvement of abiotic stress tolerance in soybean.

Keywords: abiotic stress tolerance, soybean, genomics, proteomics, transcriptomics, ionomics, phenomics

INTRODUCTIONSoybean is the most important legume crop which providessources of oil and protein for human as well as for livestock.Soybean also enhances soil fertility because of the symbiotic nitro-gen fixing ability. Soybean contributed to more than 50% ofglobally consumed edible oil (SoyStats, 20131). Apart from theconsumption, soybean oil is being considered as a future sourceof fuel and efforts are being made to improve soy-diesel produc-tion (Candeia et al., 2009). Soybean protein-based bio-degradablematerials are also being considered as an alternative for plas-tics (Song et al., 2011). Soybean products are gaining attentionbecause of its pharmaceutical attributes such as anti-cancerousproperties (Ko et al., 2013). Such diverse uses of soybean makeit a more widely desired crop plant and are rapidly increas-ing its demand. In this regard, soybean yield improvement hasbeen achieved by 1.3% per year (Ray et al., 2013). However, theincreasing global population will need double the current foodproduction by the year 2050 and at the current rate it can achieveonly ∼55% (Ray et al., 2013). It may be more difficult to pro-duce sufficient yield with the changing climate. Therefore soybeanyield prediction must consider the ongoing challenges of extreme

1Available online at: http://www.soystats.com (Accessed December 10, 2013).

weather such as drought, flood, heat, cold, frost, and possible UVstress.

Abiotic stresses are the most challenging of all major con-straints in crop production. Soybean production is not onlyinfluenced by environmental factors, such as drought, water sub-mergence, salt, and heavy metals, but it also faces challengesto get adapted in non-traditional areas. This demands extensivebreeding for the development of local cultivars (Tanksley andNelson, 1996; Grainger and Rajcan, 2013). Direct selection foryield stability based on multi-location trials has been tradition-ally used for the development of varieties adapted to adverseenvironmental conditions. This approach is more difficult for abi-otic stress related traits because of low heritability and highlyinfluenced by environmental conditions (Manavalan et al., 2009).Direct selection is also a time-consuming and labor intensive pro-cess. Strategic marker-assisted breeding can efficiently acceleratethe development of tolerant cultivars; however, it also necessi-tates knowledge about genomic loci governing the traits and theavailability of tightly linked molecular markers (Xu et al., 2012).Molecular marker development has been accelerated with theavailability of sequenced genomes and organelles in crop plants(Singh et al., 2010; Sonah et al., 2011a; Tomar et al., 2014).

Marker-assisted breeding has become sophisticated with theavailability of complete soybean genome sequence due to

www.frontiersin.org June 2014 | Volume 5 | Article 244 | 1

Deshmukh et al. Abiotic stress tolerance in soybean

subsequent development of locus-specific molecular markers(Schmutz et al., 2010; Song et al., 2010). Genome-wide highdensity markers availability also facilitates the haplotype analysisand identification of different alleles for agronomical impor-tant traits (Tardivel et al., 2014). Marker-assisted breeding hasbeen carried-out mostly for simple traits governed by a sin-gle, or at most a few loci (Shi et al., 2009; Jun et al., 2012).Marker-assisted breeding also suffers due to undesired geneticdrag (Tanksley and Nelson, 1996; Shi et al., 2009). The geneticbackground of the recurrent parent also plays an important rolein the phenotypic expression of newly introgressed gene(s) mostlybecause of the complex epistatic interaction (Palloix et al., 2009).In the case of multiple complex traits, epistatic interaction ismore unpredictable and it is hard to develop a strategic breed-ing plan until unless solid information is available about themolecular mechanisms involved in the trait development. Recenttechnological development in genomics provides tremendouspower to predict genetic factors, their evolution, distribution,and interactions at great extent (Morrell et al., 2011; Sonahet al., 2011b). Genetic engineering is the most advanced approachthat has been used for the genetic improvement of soybean.Genetically modified (GM) soybean crops for insect-resistanceand herbicide-tolerance has covered most of the cultivated area inthe world (Carpenter, 2010). Although, GM soybean has provento be very successful, it raises ethical controversies, and it isavailable only for few traits (Carpenter, 2010). Integration ofmulti-disciplinary knowledge is required to design future soybeanvarieties with ideal plant types providing high and stable yield inadverse climatic conditions. In this context, a detailed review wasmade to evaluate progress achieved in different omic approachesand to highlight future perspectives for its effective explo-ration toward the development of abiotic stress tolerant soybeancultivars.

OMICS APPROACHES IN THE TECHNOLOGICAL ERAPlant molecular biology aims to study cellular processes, theirgenetic control, and interactions with environmental changes.Such a multi-dimensional and detailed investigation requireslarge-scale experiments involving entire genetic, structural, orfunctional components. These large scale studies are called“omics.” Major components of omics include genomics, tran-scriptomics, proteomics, and metabolomics (Figure 1). Theseomics approaches are routinely used in various research dis-ciplines of crop plants, including soybean. Omics approacheshave improved very rapidly during the last decade as technol-ogy advances. Subsequently, high-throughput data developed byomic experiments require extensive computational resources forstorage and analysis. Thus, several online databases, analysisservers, and omics platforms have been developed. Omics is get-ting broader coverage and it is anticipated that several new omicfields will evolve in near future.

GENOMICS ADVANCES FOR ABIOTIC STRESS TOLERANCEIN SOYBEANMOLECULAR MARKER RESOURCESGenomic applications in soybean have become more standardwith the availability of whole genome sequence (WGS) (Schmutzet al., 2010). The WGS provided the basis for the development of

thousands of simple sequence repeat (SSR) markers and millionsof single nucleotide polymorphism (SNP) markers (Song et al.,2010; Sonah et al., 2013). Recent developments in next gen-eration sequencing (NGS) technologies make sequencing-basedgenotyping cost effective and efficient. Three main complexityreduction methods, namely Reduced Representation Libraries(RRLs), Restriction site Associated DNA (RAD) sequencing,and Genotyping-by-Sequencing (GBS) are being routinely used.Among these, GBS is gaining more attention because of itssimplified and cost effective methodology (Elshire et al., 2011;Sonah et al., 2012). The GBS approach has been successfullyused in several crop species (Poland and Rife, 2012). Recently,GBS methodology has been improved and streamlined for soy-bean (Sonah et al., 2013). However, sequencing-based genotypingmethods require computational expertise and significant time fordata analysis. This restricts its use in marker-assisted breedingwhere timely selection is very important. GBS will be widely usedin the future with an increasing number of software packages andcomputational pipelines (Sonah et al., 2013).

Technological advances have also provided a high-throughput,reliable, and quick array-based genotyping platforms. The SNParray development require initial information about SNPs, for-tunately, information about millions of SNPs is already avail-able in the public domain (Table 1). The Illumina Infiniumarray (SoySNP50K iSelect BeadChip) for ∼50,000 SNPs hasbeen successfully developed and used for the genotyping of sev-eral soybean plant introduction (PI) lines (Song et al., 2013).Technological advances beyond this make it possible to re-sequence hundreds of lines in a cost effective manner and hasstarted a new era of genotyping by re-sequencing (Lam et al.,2010; Li et al., 2013; Xu et al., 2013). Now, the challenge forplant biologists is how to effectively use these resources formarker-assisted applications.

QTL MAPPING FOR ABIOTIC STRESS TOLERANCE IN SOYBEANGenetic fingerprinting, linkage mapping, and quantitative traitloci (QTL) mapping are marker based applications that havebecome more sophisticated with the availability of differentgenotyping platforms (Table 1). Consequently, several effortshave been made to identify QTL for abiotic stress tolerancein soybean (Table S1). QTL studies have identified thousandsof QTL spanning the entire genome (www.soykb.org, www.

soybase.org). This is due to the complex inheritance of abioticstress tolerance which has identified unstable QTL across differ-ent environments. Further utilization of QTL information formarker-assisted breeding or candidate gene identification hasbecome difficult due to this complexity. Statistical tools suchas “Meta-QTL analysis” have been advanced that compile QTLdata from different studies together on the same linkage mapfor identification of precise QTL region (Deshmukh et al., 2012;Sosnowski et al., 2012). Several efforts have been performed toidentify meta-QTL for different agronomical and quantitativetraits in soybean (Table 2). Meta-analysis studies are still requiredexclusively for abiotic traits.

GENOME-WIDE ASSOCIATION STUDIES (GWAS) IN SOYBEANQTL mapping using bi-parental populations has limitationsbecause of restricted allelic diversity and genomic resolution.

Frontiers in Plant Science | Plant Genetics and Genomics June 2014 | Volume 5 | Article 244 | 2

Deshmukh et al. Abiotic stress tolerance in soybean

FIGURE 1 | Important branches of omics with their major components being used in different integrated approaches in soybean.

Table 1 | List of significant studies performed to develop SNP markers and subsequent genotyping using different technological platforms in

soybean.

Sr. No Genotyping platform/Approach Genotypes SNPs References

1 Illumina GoldenGate assay 3 RIL mapping populations 384 Hyten et al., 2008

2 Illumina Infinium SoySNP6K BeadChip 92 RILs 5376 Akond et al., 2013

3 Illumina genome analyzer/ReducedRepresentation Libraries (RRLs)

5 diverse genotypes 14,550 Varala et al., 2011

4 Illumina GoldenGate assay 3 RIL mapping populations 1536 Hyten et al., 2010b; Vuong et al.,2010

5 Illumina genome analyzer /RRLs 444 RILs 25,047 Hyten et al., 2010a

6 Illumina GAIIx/Genotyping by sequencing(GBS)

8 diverse genotypes 10,120 Sonah et al., 2013

7 Illumina Genome Analyzer II/whole genomere-sequencing

17 wild and 14 cultivated 2,05,614 Lam et al., 2010

8 Illumina Genome Analyzer II/whole genomere-sequencing

25 diverse genotypes 51,02,244 Li et al., 2013

9 Illumina genome analyzer/RRLs Parental lines of mapping population 39,022 Wu et al., 2010

10 Illumina Infinium BeadChip 96 each of landraces, elite cultivars and wildaccessions

52,041 Song et al., 2013

The allelic diversity can be increased to some extent byusing multi-parental crosses. Recently, Multi-parent AdvancedGeneration Inter-Cross populations (MAGIC) has been usedto identify QTL for blast and bacterial blight resistance,salinity and submergence tolerance, and grain quality traitsin rice (Bandillo et al., 2013). Such multi-parental popula-tions has mapping resolution limitations since it depends onmeiotic events (crossing-over) (Kover et al., 2009). In con-trast, the genome-wide association study (GWAS) approachprovides opportunities to explore the tremendous allelicdiversity existing in natural soybean germplasm. Mappingresolution of GWAS is also higher since millions of crossing

events have been accumulated in the germplasm duringevolution.

GWAS is routinely being used in many plant species, but onlya few studies have been reported in soybean (Table S2). Thesestudies were performed with limited markers and genotypes.GWAS in soybean is lagging behind compared to maize, mostlybecause of the slow linkage disequilibrium (LD) decay (Hytenet al., 2007; Mamidi et al., 2011). Another serious problem is theconfounding population structure since it may cause spuriousassociations leading to an increased false-discovery rate (FDR).Studies that involve case-control phenotypes (binary) carefullyrelate the cases and controls to minimize confounding effects.

www.frontiersin.org June 2014 | Volume 5 | Article 244 | 3

Deshmukh et al. Abiotic stress tolerance in soybean

Table 2 | Meta-QTL studies performed for different traits in soybean.

Sr. No Trait Meta QTL QTL compiled Studies compiled References

1 Soybean cyst nematode resistance 7 62 17 Guo et al., 2006

2 Soybean cyst nematode resistance 16 151 19 Zhang et al., 2010

3 Seed oil content 20 121 22 Qi et al., 2011b

4 Seed oil content 25 130 39 Qi et al., 2011a

5 100-seed weight 17 65 12 Zhao-Ming et al., 2009

6 100-seed weight 15 117 13 Sun et al., 2012a

7 Fungal disease resistance 23 107 23 Wang et al., 2010

8 Insect resistance 20 81 – Jing et al., 2009

9 Seed protein content 23 107 29 Zhao-Ming et al., 2011

10 Plant height 12 93 13 Sun et al., 2012b

11 Phosphorus efficiency 29 96 – Huang et al., 2011

12 Growth stages 9 98 10 Qiong et al., 2009

GWAS for quantitative traits like abiotic stress tolerance are pre-dictable to be affected by a confounding population. Differentmodels have been developed for population stratification andspurious allelic associations like MLM and CMLM which takesinto account the population structure and kinship. Recently,GWAS for Sclerotinia sclerotiorum resistance was performed using7864 SNPs in soybean (Bastien et al., 2014). The study provideddetails of a probable marker requirement and methodologiesinvolving population stratification for effective GWAS (Bastienet al., 2014). Development in statistical tools, genotyping meth-ods, and studies involving larger sets of genotypes will definitelyimprove GWAS power in soybean.

GENOMIC SELECTION (GS) IN SOYBEANMarker-assisted breeding for simple Mendelian traits are easyand effective, but it can be problematic for the complex traitssuch as abiotic stresses that are generally polygenic. Even majorQTLs can explain only a small fraction of phenotypic variationand may show unexpected trait expression in new genetic back-grounds because of epistatic interactions. These limitations can beeffectively addressed by the use of an approach called “Genomic-selection” (GS). GS is relatively simple, more reliable, and a morepowerful approach where breeding values of lines are predictedusing their phenotypes and marker genotypes (Heffner et al.,2009). GS is more effective since it uses all marker informationsimultaneously to develop a prediction model avoiding biasedmarker effects (Heffner et al., 2009). GS captures small-effect QTLthat governs most of the variation including epistatic interactioneffects.

An overview of research articles regarding GS published dur-ing last decade showed exponential growth within recent years(Figure S1). The increasing popularity of GS among plant aswell as animal breeders is mostly because of the reduced cost ofgenotyping. Currently, GS is being used for breeding in severaldifferent crops (Table S3). In soybean, efforts have been made toevaluate GS using different models. A GS study in soybean hasused 126 recombinant inbred lines and 80 SSR markers to pre-dict primary embryogenesis capacity which is a highly polygenictrait (Hu et al., 2011). In this report, high correlation (r2 = 0.78)has been observed among the genomic estimated breeding value

(GEBV) and the phenotypic value. Another study publishedrecently using 288 cultivars and 79 SSR markers, found a correla-tion coefficient of 0.90 among the GEBV and the phenotypic value(Shu et al., 2012). Both the reports have shown high accuracy ofprediction but only with a few markers and genotypes. Predictingthe accuracy of GS will need more investigations involving high-throughput genotyping of larger populations evaluated acrossdifferent environments.

Accuracy of GS largely depends on genetic × environmen-tal (G × E) interaction but most of the studies focused only onan estimation of the main effect for each marker. These multi-environmental trials are of prime importance for plant breedingnot only to study G × E but especially to increase the num-ber of breeding cycles per year. The challenge for GS is to getaccurate GEBV in respect to the G × E effect. Considering envi-ronmental effects is not new for plant breeders and most statisticalmodels used for multi-location trials do reflect G × E (Hammeret al., 2006). It is also more common in QTL mapping studieswhere QTL × environment interaction evaluations were utilizedto estimate QTL effect.

Improved factorial regression models have been proposedrecently for GS that consider stress covariates derived fromdaily weather data (Heslot et al., 2014). This model has shownincreased accuracy by 11.1% for predicting GEBV in unobservedenvironments where weather data is available (Heslot et al., 2014).This study suggests possible utilization of phenotypic data andhistorical data of weather conditions accumulated over decadesin different soybean breeding programs. Similar information canbe used for abiotic stress tolerance improvement in soybean.

COMBINING MARKER-ASSISTED BREEDING WITH GENOMICSELECTIONMolecular marker genotyping is a common requirement for QTLmapping, GWAS, and GS and can be the basis for combining theseapproaches (Figure 2). Most of the GS studies have used recombi-nant inbred line (RIL) populations to train the prediction model(Table S3). Therefore, GS and QTL mapping can be performedsimultaneously. A set of diverse cultivars can be used for GWASand GS all together (Table S3). In the marker-assisted breed-ing, introgression of QTL or GWAS loci to well adapted cultivar

Frontiers in Plant Science | Plant Genetics and Genomics June 2014 | Volume 5 | Article 244 | 4

Deshmukh et al. Abiotic stress tolerance in soybean

FIGURE 2 | Combined approach of QTL mapping/Genome-wide

association study (GWAS) and Genomic selection (GS).

is performed. The donor line (for QTL or GWAS loci) may bewild or low yielding line. Therefore, several cycles of backcrossingare performed to retain the genetic background of the recipi-ent parent (the adapted cultivar) except for the QTL/GWAS lociwhich represent the donor background. Nevertheless, GS doesnot provide control over the genetic background and this may beproblematic when the donor is not an adapted line. In addition,GS cannot guarantee for major QTL which are already known.Therefore, information about QTL/GWAS loci should be incor-porated with GS models so that the balance of genetic backgroundcan be made along with maximum gain of breeding value.

TRANSCRIPTOME PROFILING FOR ABIOTIC STRESSTOLERANCEPlants, including soybean, responses to external environments isvery complex. A wide range of defense mechanisms are activatedthat increases plant tolerance against adverse conditions in orderto avoid damage imposed by abiotic stresses. The first step towardstress response is stress signal recognition and subsequent molec-ular, biochemical, and physiological responses activated throughsignal transduction (Komatsu et al., 2009; Ge et al., 2010; Leet al., 2012). Understanding such responses is very important for

effective management of abiotic stress. Transcriptome profilingprovides an opportunity to investigate plant response regula-tion and to identify genes involved in stress tolerance mecha-nisms. Earlier, approaches using expressed sequence tags (ESTs)sequencing along with several techniques, such as suppressionsubtractive hybridization (SSH), have been extensively used fortranscriptome profiling of soybean under abiotic stress condi-tions (Clement et al., 2008). In addition, information of ESTshave been used to develop spotted microarrays (O’Rourke et al.,2007). These techniques are efficient but do not ensure analysisof entire genes in the soybean genome. Several high-throughputtechniques have been developed for transcriptome analysis dueto the advancement in sequencing technology and the availabil-ity of the whole soybean genome sequence, (Libault et al., 2010;Schmutz et al., 2010; Cheng et al., 2013). These platforms havebeen extensively used for transcriptome profiling to uplift abioticstress tolerance mechanisms in soybean (Table 3).

Microarray is a high-throughput technology where thousandsof probes representing different genes are hybridized with RNAsamples. Using the hybridization signal level, gene expressionis calculated. The Affymetrix GeneChip representing 61K probesets is routinely being used for transcriptome profiling of soy-bean under different abiotic stresses (Haerizadeh et al., 2011; Leet al., 2012). The normalized expression data generated using theAffymetrix GeneChip can be used to compare soybean experi-ments performed across the world. An expression database hasbeen developed to globally explore public and proprietary expres-sion data (www.genevestigator.com). The microarray data rep-resents various tissues, developmental stages, and environmentalconditions (Table 3). Effective analysis of such tremendous datausing sequence homology and functional annotation will behelpful to understand biological processes.

RNA-Seq, AN ADVANCED APPROACH FOR TRANSCRIPTOMEPROFILINGCost effective and high-throughput sequencing technologiesmake it possible to analyze transcriptomes by sequencing, knownas RNA-seq. The RNA-seq approach has several advances over themicroarray technology where available genomic information isused to design probe sets. However, RNA-seq does not requiregene information and is capable of identifying novel transcriptsthat were previously unknown and also provides opportunitiesto analyze non-coding RNAs. The relative accuracy of microar-rays and RNA-Seq has been evaluated using proteomics andit has been shown that RNA-Seq provides a better estimateof absolute expression levels (Fu et al., 2009). Applications ofRNA-seq can be expanded further with an increased understand-ing of molecular regulations. For instance, RNA-seq is beingused for transcription start site mapping, strand-specific mea-surements, gene fusion detection, small RNA characterization,and detection of alternative splicing events (Ozsolak and Milos,2010).

RNA-Seq has been performed to investigate seven tissues andseven stages in seed development in soybean (Severin et al., 2010).This effort has generated an expression atlas for soybean geneswhich serves as a useful resource. The tissue specific expressionpattern of genes is helpful in understanding regulation and tissuespecific function.

www.frontiersin.org June 2014 | Volume 5 | Article 244 | 5

Deshmukh et al. Abiotic stress tolerance in soybean

Table 3 | Major transcriptomic analysis for the abiotic stress tolerance in soybean using different technological platforms.

Sr. No. Trait/tissue Platform DEG* Key points References

1 Soybean root development/roottips and non-meristematic tissue

Affymetrix chips containing37,500 probe sets

9148 Resource of novel targetgenes for further studiesinvolving root developmentand biology

Haerizadeh et al., 2011

2 Iron stress/root from isogeniclines

Custom array containing 9728cDNAs

48 Genes involved in DNA repairand RNA stability wereinduced

O’Rourke et al., 2007

3 Drought stress at latedevelopmental stages/V6 and R2stages under drought and control

61 K Affymetrix Soybean ArrayGeneChip

3276 for V63270 for R2

Expression of many GmNACand hormone-related geneswas altered by drought in V6and/or R2 leaves

Le et al., 2012

4 Herbicide resistance/plant underatrazine and bentazon stress

cDNA microarraywith 36,760 different cDNAclones

6646 Expression of genes relatedto cell recovery, suchribosomal components

Zhu et al., 2009

5 Saline-alkaline stresstolerance/NaCl and NaHCO3

treatments

AffymetrixSoybean GeneChip 9027 Genes with alteredexpression regulated byalkaline stress

Ge et al., 2010

6 Flooding stress HiCEP (29,388) high coverageexpression profiling

97 genesand 34proteins

Combined approach withproteomics

Komatsu et al., 2009

*Differentially expressed genes.

COMBINING QTL MAPPING, GWAS, AND TRANSCRIPTOME PROFILINGQTL mapping and GWAS are very effective approaches to identifychromosomal region(s) associated with a particular phenotype.However, QTL spans large segments of chromosomes and it is alsothe same for GWAS where LD decay is slow as in case of soybean(Hyten et al., 2007). QTL or GWAS loci possess hundreds of genesthat make the identification of candidate genes difficult (Sonahet al., 2012). This is similar in transcriptome profiling where thou-sands of genes have been found to be differentially expressed evenwith genetically similar isogenic lines (Table 3). Therefore com-bining QTL mapping or GWAS with transcriptome profiling willcomplement each other. For instance, candidate genes for grainnumber QTL in rice have been identified using microarray basedtranscriptome profiling of recombinant inbreed lines with con-trasting phenotypes (Deshmukh et al., 2010; Sharma et al., 2011;Kadam et al., 2012). Similarly, a pair of soybean near-isogeniclines (NILs) differing in seed protein and an introgressed QTLsegment (∼8.4 Mb) have been used to study variation in tran-script abundance in the developing seed (Bolon et al., 2010).The study identified 13 candidate genes in the QTL region usingthe Affymetrix Soy GeneChip and high-throughput Illuminawhole transcriptome sequencing (Bolon et al., 2010). A combinedapproach of mapping and transcriptome profiling is based on anassumption that the quantitative trait is regulated by differentialexpression of candidate genes. This is not always true. Most ofthe time sequence variation present in candidate genes may causedefective proteins (Xu et al., 2013). Therefore, re-sequencing ofQTL locus along with transcriptomics will also be a valuableapproach to compliment mapping efforts.

PROTEOMICS IN SOYBEANProteomics deals with structural and functional features of allthe proteins in an organism. It is important to understand

complex biological mechanisms including the plant responsesto abiotic stress tolerance. Abiotic stress tolerance mechanismsinvolve stress perception, followed by signal transduction, whichchanges expression of stress-induced genes and proteins. Post-translational changes are also important in plant responses toabiotic stresses. A single gene can translate in several differentproteins and a few genes can lead to a diverse proteome. Suchinconsistency limits genomics and transcriptomic approachesmore specifically, when post translational changes govern phe-notype. Differential expression observed at the transcriptional(mRNA) level need not be translated into differential amountsof protein. To address this, several proteomic studies have beenperformed to understand abiotic stress tolerance mechanisms insoybean (Table S4).

Unexpected levels of changes in the soybean proteome canoccur during stress response and these changes can lead to dif-ferent defense mechanisms. Some common proteins involved inredox systems, carbon metabolism, photosynthesis, signaling, andamino acid metabolism have been found to be associated withvarious stress responses in soybean (Zhen et al., 2007; Aghaeiet al., 2009; Yamaguchi et al., 2010; Qin et al., 2013). These can-didate proteins can directly link to genetic regulation of stressresponse in soybean. Candidate protein information can be usedfor the functional annotation of genes present in QTL regions orfound differentially expressed under stress conditions.

In the near future, various proteomics approaches will beroutinely used in soybean research that will generate tremen-dous information regarding structural and functional attributesof proteins. A systematic cataloging of information in the formof a publically accessible database is very important. Recently, aproteome database has been developed that contains referencemaps of the soybean proteome collected from several organs, tis-sues, and organelles (Mooney and Thelen, 2004; Brechenmacher

Frontiers in Plant Science | Plant Genetics and Genomics June 2014 | Volume 5 | Article 244 | 6

Deshmukh et al. Abiotic stress tolerance in soybean

et al., 2009; Ohyanagi et al., 2012). Presently, these reference mapscomprised information of about 3399 proteins from seven organsand 2019 proteins from four subcellular compartments thatwere identified using two-dimensional electrophoresis (http://proteome.dc.affrc.go.jp/soybean/). Volunteer deposition of pro-teomic information in such databases is necessary for effectiveutilization of available knowledge for the management of abioticstress tolerance in soybean.

METABOLOMICS ADVANCES FOR ABIOTIC STRESSMetabolomic studies in plants aim to identify and quantify thecomplete range of primary and secondary metabolites involvedin biological processes. Therefore metabolomics provides a betterunderstanding of biochemical pathways and molecular mecha-nisms. The knowledge of genes, transcripts and proteins involvedcannot alone help to understand the biological process com-pletely until knowledge of metabolites that are involved becomesavailable.

Several metabolomics studies have been performed to under-stand biochemical processes in soybean (Table S5). Developmentof new chromatographic and mass spectrometric platforms alongwith the enhancement of operational and analytical capabilitiesof existing platforms revolutionizes metabolomic investigationsboth in plant and animal sciences. The platforms such as gaschromatography mass spectrometry (GC-MS), fourier transformion cyclotron resonance mass spectrometry (FT-ICR-MS), liq-uid chromatography mass spectrometry (LC-MS), capillary elec-trophoresis mass spectrometry (CE-MS), and nuclear magneticresonance (NMR) are routinely used in plant sciences (Putri et al.,2013). Capability, limitations and specificity of these techniqueshas been recently reviewed in terms of effective utilization of these

advanced resources (Putri et al., 2013). In-depth accurate anal-yses of metabolite information including the spectral data arethe major challenge for the use of high-throughput techniques.Several statistical models and bioinformatics programs have beendeveloped to analyze the metabolome in an interactive manner(Fernie et al., 2011; Putri et al., 2013).

IONOMICS IN SOYBEANIonomics is the study of elemental composition of an organ-ism that mostly deals with high-throughput identification andquantification. Ionomics is important to understand elementcomposition and their role in biochemical, physiological func-tionality and nutritional requirements of plants. Phosphorus (P)and potassium (K) are the two key elements used as macronu-trients in fertilizer to ensure better crop yield. However plantsrequire many other elements and those are not uniformly dis-tributed among different soil types. Plants have evolved with adiverse element uptake ability at different locations because ofdiverse soil types (Fujita et al., 2013). This justifies the need ofintegrating ionomics with genomics to explore existing geneticdifferences. An ionomic study has been performed to analyzeconcentrations of 17 different elements in diverse accessions andthree RIL populations of Arabidopsis thaliana grown in severaldifferent environments (Buescher et al., 2010). Significant differ-ences in elemental composition between the Arabidopsis acces-sions were detected and more than hundred QTL were identifiedfor different elemental accumulation (Buescher et al., 2010). Mostof the ionomics studies to date in soybean have been performedto analyze nutritive value of soybean products (Table S6).

The elemental composition of a plant is controlled by multiplefactors including element availability, uptake capability of roots,

FIGURE 3 | Phenomics and its integration with other omics approaches.

www.frontiersin.org June 2014 | Volume 5 | Article 244 | 7

Deshmukh et al. Abiotic stress tolerance in soybean

transport, and external environment which regulate physiologi-cal processes such as evapotranspiration. Because of such factors,the plant ionome has become very sensitive and specific so thatthe element profile reflects different physiological states. Recentlya study performed in barley has analyzed ionome of wild acces-sions and cultivar differing in salt tolerance, grown in presence of150 and 300 mM NaCl (Wu et al., 2013) and observed decreasedamounts of K, magnesium (Mg), P and manganese (Mn) in rootsand K, calcium (Ca), Mg and Sulfur (S) in shoots at the seedlingstage. In addition, significant negative correlation among theamount of accumulated Na and metabolites involved in glycol-ysis and tricarboxylic acid (TCA) cycle have been observed (Wuet al., 2013). This ionomic study suggests the possible rearrange-ment of elemental profiles and metabolic processes to modify thephysiological mechanisms of salinity tolerance.

Improvement in abiotic stress tolerance with the application ofseveral inorganic element has been observed (Liang et al., 2007;Pilon-Smits et al., 2009). For instance, silicon (Si) has shownbeneficial effects against different abiotic stresses including highsalinity, water stress, heavy metal stress, and UV-b (Liang et al.,2007). Previously, soybean has been considered as poor accumu-lator of silicon mostly because of the genetic differences existingin the germplasm and very few genotypes have been evaluatedto draw this conclusion (Hodson et al., 2005). However, with theadvancement in ionomics technologies, silicon transporter geneshave been identified recently in soybean using the integratedomics approach (Deshmukh et al., 2013). This study has usedcomputational genomics, transcriptomics, and ionomics infor-mation available in the model plant species such as Arabidopsisand rice. Besides this, high-throughput efforts for maximumnumber of elemental profiles in soybean in respective externalenvironment are required. That will definitely improve the under-standing of the soybean ionome and its subsequent utilization inthe management of abiotic stress tolerance.

PHENOMICS PROSPECTIVE IN SOYBEANThe phenotype is a physical and biochemical trait of an organ-ism. Phenomics is a study involving high-throughput analysis ofphenotype. Phenotype is the ultimate resultant from the complexinteractions of genetic potential between an organism and envi-ronment. Precision phenotyping is important to understand anybiological system. In plant as well as animal sciences, a partic-ular phenotype (as symptoms) is used to understand biologicalstatus, such as disease, pest infestation or physiological disor-ders. With technological advances, genomic resources have beenroutinely used to predict phenotype based on the evaluation ofgenetic markers; it can be called “genetic symptoms.” The successof genomics is based on how reliable connection is there betweena genetic marker and the phenotype. In plant breeding, geneticimprovement through omics approaches is being conducted toachieve ideal phenotype that will ensure higher and stable yieldunder diverse environmental conditions. Therefore phenomicsintegrated with other omics approaches has the most potentialin the plant breeding (Figure 3).

Phenome has a broader meaning than what is being generallyconsidered. It is not limited to the visible morphology of anorganism but expectedly larger and complex. Unlike genomics,

where the entire genome can be characterized by sequencing, thephenome cannot be characterized entirely. Therefore, the termphenomics being an analogy to genomics expected only study ofparticular set of phenotype at high-throughput level and not theentire set. In this regards, the technological development in imageprocessing and the automation techniques have played impor-tant roles. Plant imaging with light sources from visible to nearinfrared spectrum provides an opportunity for non-destructivephenotyping. Therefore, real-time analysis of plant developmentbecame possible. Moreover, robotic technologies used in phe-nomic platforms have increased the precision and speed of phe-notyping. This has allowed for incorporating additional aidssuch as precise irrigation and fertilization systems. For instance,“PHENOPSIS” an automated phenomic platform has been devel-oped to study water stress in Arabidopsis and has a robotic armloaded with a tube for irrigation and a camera (Granier et al.,2006). These types of advanced phenomic platforms have beendeveloped and made available for wider range of crop plants(www.lemnatec.com). However, these platforms have not gainedthe expected popularity even though tremendous advancement inboth imaging as well as robotic technology has been achieved.

In soybean, several phenomic efforts have been performed butmost of these are pilot experiments (Table S7). Recently, a methodhas been developed to assess leaf growth in soybean under dif-ferent environmental conditions (Mielewczik et al., 2013). Thismethod can utilize different light sources that are available ina greenhouse as well as under field conditions. Marker track-ing approaches (Martrack Leaf) have also been used to facilitateaccurate analysis of two-dimensional leaf expansion with hightemporal resolution (Mielewczik et al., 2013). Apart from this,phenomics has been used to facilitate efficient identification ofsoybean cultivars which is very important for germplasm resourcemanagement and utilization (Zhu et al., 2012). Zhu et al. (2012),used a laser light back-scattering imaging technology to analyzesingle seed. Images of laser light illuminated the soybean seedsurface were captured by a charge-coupled device (CCD) camera.The characteristic pattern of laser luminance is analyzed by imageprocessing technology to identify a particular cultivar. Such char-acteristic of laser light back-scattering can be used to assess qualityand other seed characteristics as markers for selection in breedingprograms.

Phenomics in soybean is lagging far behind genomics becausehundreds of genomes and many genetic populations are re-sequenced. One best example is the 1000 genome re-sequencingproject at the University of Missouri, MO, USA (http://soybeangenomics.missouri.edu/news2012.php). The 1000 genomeproject will generate a huge amount of genomic informationwhich will require utilization of comparable phenomic data. Thiswill be helpful to accelerate soybean research in many ways.

ROLE OF ONLINE DATABASES FOR EFFECTIVE INTEGRATIONOF OMICS PLATFORMSThe recent advancement in the omic platforms has gener-ated tremendous information which has been used to promoteresearch activities in all possible dimensions. Utilization of avail-able information has become possible because of computationalresources that helps to catalog, store, and analyze available

Frontiers in Plant Science | Plant Genetics and Genomics June 2014 | Volume 5 | Article 244 | 8

Deshmukh et al. Abiotic stress tolerance in soybean

Table 4 | Online databases exclusively developed to host soybean research data generated from different omics platforms.

Sr. No Database Features Tools

1 SoyBaseSoyBase and the Soybean Breeder’s Toolbox,USDA and Iowa University, http://soybase.org/

Genetic and physical maps, QTL,Genome sequence, Transposableelements, Annotations, Graphicalchromosome visualizer

BLAST search, ESTs search, SoyChipAnnotation Search, PotentialHaplotype (pHap) and Contig Search,Soybean Metabolic Pathways, FastNeutron Mutants Search, RNA-SeqAtlas

2 SoyKBSoybean Knowledge Base, University of Missouri,Columbia, http://soykb.org/

Multi-omics datasets,Genes/proteins, miRNAs/sRNAs,Metabolite profiling, Molecularmarkers, information about plantintroduction lines and traits,Graphical chromosome visualizer

Germplasm browser, QTL and Traitbrowser, Fast neutron mutant data,Differential expression analysis,Phosphorylation data, Phylogeny,Protein BioViewer, Heatmap andhierarchical clustering, PI and traitsearch, FTP/data downloadcapabilities

3 SoyDBSoybean transcription factors database, MissouriUniversity, http://casp.rnet.missouri.edu/soydb/

Protein sequences, Predictedtertiary structures, Putative DNAbinding sites, Protein Data Bank(PDB), Protein familyclassifications

PSI-BLAST, Browse database, FamilyPrediction by HMM, FTP data retriever

4 SGMDThe Soybean Genomics and Microarray Database,http://bioinformatics.towson.edu/SGMD/

Integrated view genomic, ESTand microarray data

Analytical tools allowing correlation ofsoybean ESTs with their geneexpression profiles

5 DeltasoyAn Internet-Based Soybean Database for OfficialVariety Trials,http://msucares.com/deltasoy/testlocationmap.htm

Official variety trial (OVT)information in soybean,Mississippi OVT data, includingyield, location, and diseaseinformation

Comparison tools for variety trail data,phenotypic data and disease relateddata

6 DaizuBaseAn integrated soybean genome database includingBAC-based physical maps,http://daizu.dna.affrc.go.jp/

BAC-based physical map, Linkagemap and DNA markers, BAC-end,BAC contigs, ESTs, full-lengthcDNAs

Gbrowse, Unified Map, Gene viewer,BLAST

7 SoyMetDBThe soybean metabolome database,http://soymetdb.org

Soybean metabolomic data Pathway Viewer

9 SoyProDBSoybean proteins database,http://bioinformatics.towson.edu

Several 2D Gel images showingisolated soybean seed proteins

Search tool for 2D spots, Navigationtools for protein data

10 SoyGDThe Soybean GBrowse Database, Southern IllinoisUniversity, http://soybeangenome.siu.edu/

Physical map and genetic map,Bacterial artificial chromosome(BAC) fingerprint database,Associated genomic data

Sequence data retrieval tools,Navigation tool for sequenceinformation of different builds

11 SoyTEdbSoybean transposable elements database,www.soybase.org/soytedb/

Williams 82 transposable elementdatabase

Browse for Repetitive elements,Transposable Element and Mapposition, Data retrieval tools

12 SoyXpressSoybean transcriptome database,http://soyxpress2.agrenv.mcgill.ca

Soybean ESTs, Metabolicpathways, Gene Ontology terms,Swiss-prot Identifiers andAffymetrix gene expression data

BLAST search, Microarrayexperiments, Pathway search etc

www.frontiersin.org June 2014 | Volume 5 | Article 244 | 9

Deshmukh et al. Abiotic stress tolerance in soybean

data and make it easily accessible through user friendly inter-faces so called “databases.” In this regard, several databases havebeen developed for soybean (Table 4). Among these, SoybeanKnowledge Base (SKB, http://soykb.org) is a very useful databasethat provides a comprehensive web resource for omics data fromseveral different platforms (Joshi et al., 2012). The SKB resourcesare helpful for bridging soybean translational genomics andmolecular breeding research. It contains information of genes,proteins, microRNAs, sRNAs, metabolites, molecular markers,and phenomic information of soybean plant introductions (PI).It also provides interference to integrate multi-omics datasets andbecause of this, a galaxy of information becomes comparableand more useful. For instance, genes in the QTL region can beretrieved very easily along with the functional annotations, asso-ciated protein information in respect of structure and functionalfeatures, syntenic information with other model plants, sequencevariation among different cultivars, gene expression data includ-ing tissue specific variations and many other types of informationfor soybean.

GENERAL CONCLUSIONDifferent omics tools have been employed to understand how soy-bean plants respond to abiotic stress conditions. We realize thatthe studies to integrate multiple omics approaches are limiting insoybean due to the increased cost and potential challenging inte-grated omic scale analysis. Recent developments in computationalresources, statistical tools, and instrumentation have lowered thecost of omics in many folds but integrated analysis needs noveltools and technical wizards. The comprehensive nature of multi-omic studies provides an entirely new avenue and future researchprograms should plan to adapt accordingly. In soybean, genomicsand transcriptomics have progressed as expected but the othermajor omic branches like proteomics, metabolomics, and phe-nomics are still lagging behind. These omic branches are equallyimportant to get clear picture of the biological system. Notably,phenomic studies need to be extensively employed along withthe other omics approaches. Desired phenotype is ultimate aimof crop sciences; therefore it needs to be understood intensely.Different omic tools and integrated approaches discussed in thepresent review will provide glimpses of current scenarios andfuture perspectives for the effective management of abiotic stresstolerance in soybean.

ACKNOWLEDGMENTSThe authors are thankful to Theresa Musket and Michelle Keoughfor their insight, critical reviews and language improvement. Thisresearch was supported by grants from the United Soybean Board,USA.

SUPPLEMENTARY MATERIALThe Supplementary Material for this article can be found onlineat: http://www.frontiersin.org/journal/10.3389/fpls.2014.00244/abstract

REFERENCESAghaei, K., Ehsanpour, A., Shah, A., and Komatsu, S. (2009). Proteome analysis

of soybean hypocotyl and root under salt stress. Amino Acids 36, 91–98. doi:10.1007/s00726-008-0036-7

Akond, M., Schoener, L., Kantartzi, S., Meksem, K., Song, Q., Wang, D., et al.(2013). A SNP-based genetic linkage map of soybean using the SoySNP6KIllumina Infinium BeadChip genotyping array. J. Plant Genome Sci. 1, 80–89.doi: 10.5147/jpgs.2013.0090

Bandillo, N., Raghavan, C., Muyco, P. A., Sevilla, M. A. L., Lobina, I. T., Dilla-Ermita, C. J., et al. (2013). Multi-parent advanced generation inter-cross(MAGIC) populations in rice: progress and potential for genetics research andbreeding. Rice 6, 1–15. doi: 10.1186/1939-8433-6-11

Bastien, M., Sonah, H., and Belzile, F. (2014). Genome wide association mapping ofSclerotinia sclerotiorum resistance in soybean with a genotyping by sequencingapproach. Plant Genome 7, 1–13. doi: 10.3835/plantgenome2013.10.0030

Bolon, Y.-T., Joseph, B., Cannon, S. B., Graham, M. A., Diers, B. W., Farmer, A. D.,et al. (2010). Complementary genetic and genomic approaches help characterizethe linkage group I seed protein QTL in soybean. BMC Plant Biol. 10:41. doi:10.1186/1471-2229-10-41

Brechenmacher, L., Lee, J., Sachdev, S., Song, Z., Nguyen, T. H. N., Joshi, T., et al.(2009). Establishment of a protein reference map for soybean root hair cells.Plant Physiol. 149, 670–682. doi: 10.1104/pp.108.131649

Buescher, E., Achberger, T., Amusan, I., Giannini, A., Ochsenfeld, C., Rus, A.,et al. (2010). Natural genetic variation in selected populations of Arabidopsisthaliana is associated with ionomic differences. PLoS ONE 5:e11081. doi:10.1371/journal.pone.0011081

Candeia, R., Silva, M., Carvalho Filho, J., Brasilino, M., Bicudo, T., Santos, I., et al.(2009). Influence of soybean biodiesel content on basic properties of biodiesel–diesel blends. Fuel 88, 738–743. doi: 10.1016/j.fuel.2008.10.015

Carpenter, J. E. (2010). Peer-reviewed surveys indicate positive impact of commer-cialized GM crops. Nat. Biotech. 28, 319–321. doi: 10.1038/nbt0410-319

Cheng, Y.-Q., Liu, J.-F., Yang, X., Ma, R., Liu, C., and Liu, Q. (2013). RNA-seqanalysis reveals ethylene-mediated reproductive organ development and abscis-sion in soybean (Glycine max L. Merr.). Plant Mol. Biol. Rep. 31, 607–619. doi:10.1007/s11105-012-0533-4

Clement, M., Lambert, A., Herouart, D., and Boncompagni, E. (2008).Identification of new up-regulated genes under drought stress in soybeannodules. Gene 426, 15–22. doi: 10.1016/j.gene.2008.08.016

Deshmukh, R., Singh, A., Jain, N., Anand, S., Gacche, R., Singh, A., et al. (2010).Identification of candidate genes for grain number in rice (Oryza sativa L.).Funct. Integr. Genomics 10, 339–347. doi: 10.1007/s10142-010-0167-2

Deshmukh, R. K., Sonah, H., Kondawar, V., Tomar, R. S. S., and Deshmukh, N.K. (2012). Identification of meta quantitative trait loci for agronomical traits inrice (Oryza sativa). Ind. J. Genet. Plant Breed. 72, 264–270.

Deshmukh, R. K., Vivancos, J., Guérin, V., Sonah, H., Labbé, C., Belzile, F., et al.(2013). Identification and functional characterization of silicon transporters insoybean using comparative genomics of major intrinsic proteins in Arabidopsisand rice. Plant Mol. Biol. 83, 303–315. doi: 10.1007/s11103-013-0087-3

Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E.S., et al. (2011). A robust, simple genotyping-by-sequencing (GBS) approachfor high diversity species. PLoS ONE 6:e19379. doi: 10.1371/journal.pone.0019379

Fernie, A. R., Aharoni, A., Willmitzer, L., Stitt, M., Tohge, T., Kopka, J., et al. (2011).Recommendations for reporting metabolite data. Plant Cell 23, 2477–2482. doi:10.1105/tpc.111.086272

Fu, X., Fu, N., Guo, S., Yan, Z., Xu, Y., Hu, H., et al. (2009). Estimating accu-racy of RNA-Seq and microarrays with proteomics. BMC Genomics 10:161. doi:10.1186/1471-2164-10-161

Fujita, Y., Venterink, H. O., van Bodegom, P. M., Douma, J. C., Heil, G. W.,Hölzel, N., et al. (2013). Low investment in sexual reproduction threatens plantsadapted to phosphorus limitation. Nature 505, 82–86. doi: 10.1038/nature12733

Ge, Y., Li, Y., Zhu, Y. M., Bai, X., Lv, D. K., Guo, D., et al. (2010). Global transcrip-tome profiling of wild soybean (Glycine soja) roots under NaHCO3 treatment.BMC Plant Biol. 10:153. doi: 10.1186/1471-2229-10-153

Grainger, C. M., and Rajcan, I. (2013). Characterization of the genetic changes in amulti-generational pedigree of an elite Canadian soybean cultivar. Theor. Appl.Genet. 1–19. doi: 10.1007/s00122-013-2211-9

Granier, C., Aguirrezabal, L., Chenu, K., Cookson, S. J., Dauzat, M., Hamard,P., et al. (2006). PHENOPSIS, an automated platform for reproducible phe-notyping of plant responses to soil water deficit in Arabidopsis thalianapermitted the identification of an accession with low sensitivity to soilwater deficit. New Phytol. 169, 623–635. doi: 10.1111/j.1469-8137.2005.01609.x

Frontiers in Plant Science | Plant Genetics and Genomics June 2014 | Volume 5 | Article 244 | 10

Deshmukh et al. Abiotic stress tolerance in soybean

Guo, B., Sleper, D., Lu, P., Shannon, J., Nguyen, H., and Arelli, P. (2006). QTLsassociated with resistance to soybean cyst nematode in soybean: meta-analysisof QTL locations. Crop Sci. 46, 595–602. doi: 10.2135/cropsci2005.04-0036-2

Haerizadeh, F., Singh, M. B., and Bhalla, P. L. (2011). Transcriptome profiling ofsoybean root tips. Funct. Plant Biol. 38, 451–461. doi: 10.1071/FP10230

Hammer, G., Cooper, M., Tardieu, F., Welch, S., Walsh, B., van Eeuwijk, F., et al.(2006). Models for navigating biological complexity in breeding improved cropplants. Trends Plant Sci. 11, 587–593. doi: 10.1016/j.tplants.2006.10.006

Heffner, E. L., Sorrells, M. E., and Jannink, J. L. (2009). Genomic selection for cropimprovement. Crop Sci. 49, 1–12. doi: 10.2135/cropsci2008.08.0512

Heslot, N., Akdemir, D., Sorrells, M. E., and Jannink, J. L. (2014). Integratingenvironmental covariates and crop modeling into the genomic selection frame-work to predict genotype by environment interactions. Theor. Appl. Genet. 127,463–480. doi: 10.1007/s00122-013-2231-5

Hodson, M., White, P., Mead, A., and Broadley, M. (2005). Phylogenetic vari-ation in the silicon composition of plants. Ann. Bot. 96, 1027–1046. doi:10.1093/aob/mci255

Hu, Z., Li, Y., Song, X., Han, Y., Cai, X., Xu, S., et al. (2011). Genomic value pre-diction for quantitative traits under the epistatic model. BMC Genet. 12:15. doi:10.1186/1471-2156-12-15

Huang, L. L., Zhong, K. Z., Ma, Q. B., Nian, H., and Yang, C. Y. (2011). IntegratedQTLs map of phosphorus efficiency in soybean by Meta-analysis. Chin. J. OilCrop Sci. 33, 25–32.

Hyten, D. L., Cannon, S. B., Song, Q., Weeks, N., Fickus, E. W., Shoemaker, R. C.,et al. (2010a). High-throughput SNP discovery through deep resequencing ofa reduced representation library to anchor and orient scaffolds in the soybeanwhole genome sequence. BMC Genomics 11:38. doi: 10.1186/1471-2164-11-38

Hyten, D. L., Choi, I. Y., Song, Q., Shoemaker, R. C., Nelson, R. L., Costa, J. M., et al.(2007). Highly variable patterns of linkage disequilibrium in multiple soybeanpopulations. Genetics 175, 1937–1944. doi: 10.1534/genetics.106.069740

Hyten, D. L., Choi, I. Y., Song, Q., Specht, J. E., Carter, T. E., Shoemaker, R. C.,et al. (2010b). A high density integrated genetic linkage map of soybean and thedevelopment of a 1536 universal soy linkage panel for quantitative trait locusmapping. Crop Sci. 50, 960–968. doi: 10.2135/cropsci2009.06.0360

Hyten, D. L., Song, Q., Choi, I. Y., Yoon, M. S., Specht, J. E., Matukumalli,L. K., et al. (2008). High-throughput genotyping with the GoldenGate assayin the complex genome of soybean. Theor. Appl. Genet. 116, 945–952. doi:10.1007/s00122-008-0726-2

Jing, W., Wankun, S., Wenbo, Z., Chunyan, L., Guohua, H., and Qingshan, C.(2009). Meta-analysis of insect-resistance QTLs in soybean. Hereditas (Beijing)31, 953–961. doi: 10.3724/SP.J.1005.2009.00953

Joshi, T., Patil, K., Fitzpatrick, M. R., Franklin, L. D., Yao, Q., Cook, J. R., et al.(2012). Soybean Knowledge Base (SoyKB): a web resource for soybean transla-tional genomics. BMC Genomics 13:S15. doi: 10.1186/1471-2164-13-S1-S15

Jun, T. H., Mian, M. R., Kang, S. T., and Michel, A. P. (2012). Genetic mapping ofthe powdery mildew resistance gene in soybean PI 567301B. Theor. Appl. Genet.125, 1159–1168. doi: 10.1007/s00122-012-1902-y

Kadam, S., Singh, K., Shukla, S., Goel, S., Vikram, P., Pawar, V., et al. (2012).Genomic associations for drought tolerance on the short arm of wheat chro-mosome 4B. Funct. Integr. Genomics 12, 447–464. doi: 10.1007/s10142-012-0276-1

Ko, K. P., Park, S. K., Yang, J. J., Ma, S. H., Gwack, J., Shin, A., et al. (2013). Intakeof soy products and other foods and gastric cancer risk: a prospective study.J. Epidemiol. 23, 337. doi: 10.2188/jea.JE20120232

Komatsu, S., Yamamoto, R., Nanjo, Y., Mikami, Y., Yunokawa, H., and Sakata, K.(2009). A comprehensive analysis of the soybean genes and proteins expressedunder flooding stress using transcriptome and proteome techniques. J. ProteomeRes. 8, 4766–4778. doi: 10.1021/pr900460x

Kover, P. X., Valdar, W., Trakalo, J., Scarcelli, N., Ehrenreich, I. M., Purugganan,M. D., et al. (2009). A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 5:e1000551. doi:10.1371/journal.pgen.1000551

Lam, H. M., Xu, X., Liu, X., Chen, W., Yang, G., Wong, F. L., et al. (2010).Resequencing of 31 wild and cultivated soybean genomes identifies patterns ofgenetic diversity and selection. Nat. Genet. 42, 1053–1059. doi: 10.1038/ng.715

Le, D. T., Nishiyama, R., Watanabe, Y., Tanaka, M., Seki, M., Yamaguchi-Shinozaki,K., et al. (2012). Differential gene expression in soybean leaf tissues at late devel-opmental stages under drought stress revealed by genome-wide transcriptomeanalysis. PLoS ONE 7:e49522. doi: 10.1371/journal.pone.0049522

Li, Y. H., Zhao, S. C., Ma, J. X., Li, D., Yan, L., Li, J., et al. (2013).Molecular footprints of domestication and improvement in soybean revealed bywhole genome re-sequencing. BMC Genomics 14:579. doi: 10.1186/1471-2164-14-579

Liang, Y., Sun, W., Zhu, Y. G., and Christie, P. (2007). Mechanisms of silicon-mediated alleviation of abiotic stresses in higher plants: a review. Environ. Pollut.147, 422–428. doi: 10.1016/j.envpol.2006.06.008

Libault, M., Farmer, A., Joshi, T., Takahashi, K., Langley, R. J., Franklin, L. D., et al.(2010). An integrated transcriptome atlas of the crop model Glycine max, andits use in comparative analyses in plants. Plant J. 63, 86–99. doi: 10.1111/j.1365-313X.2010.04222.x

Mamidi, S., Chikara, S., Goos, R. J., Hyten, D. L., Annam, D., Moghaddam, S. M.,et al. (2011). Genome-wide association analysis identifies candidate genes asso-ciated with iron deficiency chlorosis in soybean. Plant Genome 4, 154–164. doi:10.3835/plantgenome2011.04.0011

Manavalan, L. P., Guttikonda, S. K., Tran, L. S. P., and Nguyen, H. T. (2009).Physiological and molecular approaches to improve drought resistance insoybean. Plant Cell Physiol. 50, 1260–1276. doi: 10.1093/pcp/pcp082

Mielewczik, M., Friedli, M., Kirchgessner, N., and Walter, A. (2013). Diel leafgrowth of soybean: a novel method to analyze two-dimensional leaf expansionin high temporal resolution based on a marker tracking approach (MartrackLeaf). Plant Methods 9, 30. doi: 10.1186/1746-4811-9-30

Mooney, B. P., and Thelen, J. J. (2004). High-throughput peptide mass finger-printing of soybean seed proteins: automated workflow and utility of UniGeneexpressed sequence tag databases for protein identification. Phytochemistry 65,1733–1744. doi: 10.1016/j.phytochem.2004.04.011

Morrell, P. L., Buckler, E. S., and Ross-Ibarra, J. (2011). Crop genomics: advancesand applications. Nat. Rev. Genet. 13, 85–96. doi: 10.1038/nrg3097

Ohyanagi, H., Sakata, K., and Komatsu, S. (2012). Soybean Proteome Database2012: update on the comprehensive data repository for soybean proteomics.Front. Plant Sci. 3:110. doi: 10.3389/fpls.2012.00110

O’Rourke, J., Charlson, D., Gonzalez, D., Vodkin, L., Graham, M., Cianzio, S., et al.(2007). Microarray analysis of iron deficiency chlorosis in near-isogenic soybeanlines. BMC Genomics 8:476. doi: 10.1186/1471-2164-8-476

Ozsolak, F., and Milos, P. M. (2010). RNA sequencing: advances, challenges andopportunities. Nat. Rev. Genet. 12, 87–98. doi: 10.1038/nrg2934

Palloix, A., Ayme, V., and Moury, B. (2009). Durability of plant major resistancegenes to pathogens depends on the genetic background, experimental evi-dence and consequences for breeding strategies. New Phytol. 183, 190–199. doi:10.1111/j.1469-8137.2009.02827.x

Pilon-Smits, E. A., Quinn, C. F., Tapken, W., Malagoli, M., and Schiavon, M.(2009). Physiological functions of beneficial elements. Curr. Opin. Plant Biol.12, 267–274. doi: 10.1016/j.pbi.2009.04.009

Poland, J. A., and Rife, T. W. (2012). Genotyping-by-sequencing for plant breedingand genetics. Plant Genome 5, 92–102. doi: 10.3835/plantgenome2012.05.0005

Putri, S. P., Yamamoto, S., Tsugawa, H., and Fukusaki, E. (2013). Currentmetabolomics: technological advances. J. Biosci. Bioeng. 116, 9–16. doi:10.1016/j.jbiosc.2013.01.004

Qi, Z. M., Han, X., Sun, Y. N., Wu, Q., Shan, D. P., Du, X. Y., et al. (2011a). Anintegrated quantitative trait locus map of oil content in soybean, (Glycine maxL.) Merr., generated using a meta-analysis method for mining genes. Agric. Sci.China 10, 1681–1692. doi: 10.1016/S1671-2927(11)60166-1

Qi, Z. M., Wu, Q., Han, X., Sun, Y. N., Du, X. Y., Liu, C. Y., et al.(2011b). Soybean oil content QTL mapping and integrating with meta-analysismethod for mining genes. Euphytica 179, 499–514. doi: 10.1007/s10681-011-0386-1

Qin, J., Gu, F., Liu, D., Yin, C., Zhao, S., Chen, H., et al. (2013). Proteomic anal-ysis of elite soybean Jidou17 and its parents using iTRAQ-based quantitativeapproaches. Proteome Sci. 11, 12. doi: 10.1186/1477-5956-11-12

Qiong, W., Zhaoming, Q., Chunyan, L., Guohua, H., and Qingshan, C. (2009).An integrated QTL map of growth stage in soybean [Glycine max (L.) Merr.]:constructed through meta-analysis. Acta Agronomica Sinica 35, 1418–1424. doi:10.3724/SP.J.1006.2009.01418

Ray, D. K., Mueller, N. D., West, P. C., and Foley, J. A. (2013). Yield trends areinsufficient to double global crop production by 2050. PLoS ONE 8:e66428. doi:10.1371/journal.pone.0066428

Schmutz, J., Cannon, S. B., Schlueter, J., Ma, J., Mitros, T., Nelson, W., et al. (2010).Genome sequence of the palaeopolyploid soybean. Nature 463, 178–183. doi:10.1038/nature08670

www.frontiersin.org June 2014 | Volume 5 | Article 244 | 11

Deshmukh et al. Abiotic stress tolerance in soybean

Severin, A. J., Woody, J. L., Bolon, Y. T., Joseph, B., Diers, B. W., Farmer, A. D., et al.(2010). RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome.BMC Plant Biol. 10:160. doi: 10.1186/1471-2229-10-160

Sharma, A., Deshmukh, R. K., Jain, N., and Singh, N. K. (2011). Combining QTLmapping and transcriptome profiling for an insight into genes for grain numberin rice (Oryza sativa L.). Ind. J. Genet. Plant Breed. 71, 115–119.

Shi, A., Chen, P., Li, D., Zheng, C., Zhang, B., and Hou, A. (2009). Pyramiding mul-tiple genes for resistance to soybean mosaic virus in soybean using molecularmarkers. Mol. Breed. 23, 113–124. doi: 10.1007/s11032-008-9219-x

Shu, Y., Yu, D., Wang, D., Bai, X., Zhu, Y., and Guo, C. (2012). Genomic selectionof seed weight based on low-density SCAR markers in soybean. Genet. Mol. Res.12, 2178–2188. doi: 10.4238/2013.July.3.2

Singh, H., Deshmukh, R. K., Singh, A., Singh, A. K., Gaikwad, K., Sharma, T. R.,et al. (2010). Highly variable SSR markers suitable for rice genotyping usingagarose gels. Mol. Breed. 25, 359–364. doi: 10.1007/s11032-009-9328-1

Sonah, H., Bastien, M., Iquira, E., Tardivel, A., Légaré, G., Boyle, B., et al. (2013).An improved genotyping by sequencing (GBS) approach offering increased ver-satility and efficiency of SNP discovery and genotyping. PLoS ONE 8:e54603.doi: 10.1371/journal.pone.0054603

Sonah, H., Deshmukh, R. K., Chand, S., Srinivasprasad, M., Rao, G. J., Upreti,H. C., et al. (2012). Molecular mapping of quantitative trait loci for flag leaflength and other agronomic traits in rice (Oryza sativa). Cereal Res. Commun.40, 362–372. doi: 10.1556/CRC.40.2012.3.5

Sonah, H., Deshmukh, R. K., Sharma, A., Singh, V. P., Gupta, D. K., Gacche, R.N., et al. (2011a). Genome-wide distribution and organization of microsatel-lites in plants: an insight into marker development in Brachypodium. PLoS ONE6:e21298. doi: 10.1371/journal.pone.0021298

Sonah, H., Deshmukh, R. K., Singh, V. P., Gupta, D. K., Singh, N. K., andSharma, T. R. (2011b). Genomic resources in horticultural crops: status, util-ity and challenges. Biotechnol. Adv. 29, 199–209. doi: 10.1016/j.biotechadv.2010.11.002

Song, F., Tang, D. L., Wang, X. L., and Wang, Y. Z. (2011). Biodegradable soy pro-tein isolate-based materials: a review. Biomacromolecules 12, 3369–3380. doi:10.1021/bm200904x

Song, Q., Hyten, D. L., Jia, G., Quigley, C. V., Fickus, E. W., Nelson, R. L., et al.(2013). Development and evaluation of SoySNP50K, a high-density genotypingarray for soybean. PLoS ONE 8:e54985. doi: 10.1371/journal.pone.0054985

Song, Q., Jia, G., Zhu, Y., Grant, D., Nelson, R. T., Hwang, E. Y., et al. (2010).Abundance of SSR motifs and development of candidate polymorphic SSRmarkers (BARCSOYSSR_1. 0) in soybean. Crop Sci. 50, 1950–1960. doi:10.2135/cropsci2009.10.0607

Sosnowski, O., Charcosset, A., and Joets, J. (2012). BioMercator V3: an upgrade ofgenetic map compilation and quantitative trait loci meta-analysis algorithms.Bioinformatics 28, 2082–2083. doi: 10.1093/bioinformatics/bts313

Sun, Y. N., Luan, H., Qi, Z., Shan, D., Liu, C., Hu, G., et al. (2012b). Mapping andmeta-analysis of height QTLs in soybean. Legume Genomics Genet. 3, 1–7. doi:10.5376/lgg.2012.03.0001

Sun, Y. N., Pan, J.-B., Shi, X. L., Du, X. Y., Wu, Q., Qi, Z. M., et al. (2012a). Multi-environment mapping and meta-analysis of 100-seed weight in soybean. Mol.Biol. Rep. 39, 9435–9443. doi: 10.1007/s11033-012-1808-4

Tanksley, S., and Nelson, J. (1996). Advanced backcross QTL analysis: a methodfor the simultaneous discovery and transfer of valuable QTLs from unadaptedgermplasm into elite breeding lines. Theor. Appl. Genet. 92, 191–203. doi:10.1007/BF00223376

Tardivel, A., Sonah, H., Belzile, F., and O’Donoughue, L. S. (2014). Rapididentification of alleles at the soybean maturity gene E3 using genotypingby sequencing and a haplotype-based approach. Plant Genome 7, 1–9. doi:10.3835/plantgenome2013.10.0034

Tomar, R. S. S., Deshmukh, R. K., Naik, K., Tomar, S. M. S., and Vinod (2014).Development of chloroplast−specific microsatellite markers for molecular char-acterization of alloplasmic lines and phylogenetic analysis in wheat. Plant Breed.133, 12–18. doi: 10.1111/pbr.12116

Varala, K., Swaminathan, K., Li, Y., and Hudson, M. E. (2011). Rapid genotyping ofsoybean cultivars using high throughput sequencing. PLoS ONE 6:e24811. doi:10.1371/journal.pone.0024811

Vuong, T. D., Sleper, D. A., Shannon, J. G., and Nguyen, H. T. (2010). Novel quanti-tative trait loci for broad-based resistance to soybean cyst nematode (Heteroderaglycines Ichinohe) in soybean PI 567516C. Theor. Appl. Genet. 121, 1253–1266.doi: 10.1007/s00122-010-1385-7

Wang, J. L., Liu, C. Y., Wang, J., Qi, Z. M., Li, H., Hu, G. H., et al. (2010). An inte-grated QTL map of fungal disease resistance in soybean (Glycine max L. Merr):a method of meta-analysis for mining R genes. Agric. Sci. China 9, 223–232. doi:10.1016/S1671-2927(09)60087-0

Wu, D., Shen, Q., Cai, S., Chen, Z. H., Dai, F., and Zhang, G. (2013). Ionomicresponses and correlations between elements and metabolites under saltstress in wild and cultivated barley. Plant Cell Physiol. 54, 1976–1988. doi:10.1093/pcp/pct134

Wu, X., Ren, C., Joshi, T., Vuong, T., Xu, D., and Nguyen, H. (2010). SNP dis-covery by high-throughput sequencing in soybean. BMC Genomics 11:469. doi:10.1186/1471-2164-11-469

Xu, X., Zeng, L., Tao, Y., Vuong, T., Wan, J., Boerma, R., et al. (2013). Pinpointinggenes underlying the quantitative trait loci for root-knot nematode resistance inpalaeopolyploid soybean by whole genome resequencing. Proc. Natl. Acad. Sci.U.S.A. 110, 13469–13474. doi: 10.1073/pnas.1222368110

Xu, Y., Lu, Y., Xie, C., Gao, S., Wan, J., and Prasanna, B. M. (2012). Whole-genome strategies for marker-assisted plant breeding. Mol. Breed. 29, 833–854.doi: 10.1007/s11032-012-9699-6

Yamaguchi, M., Valliyodan, B., Zhang, J., Lenoble, M. E., Yu, O., Rogers, E.E., et al. (2010). Regulation of growth response to water stress in the soy-bean primary root. I. Proteomic analysis reveals region−specific regulation ofphenylpropanoid metabolism and control of free iron in the elongation zone.Plant Cell Environ. 33, 223–243. doi: 10.1111/j.1365-3040.2009.02073.x

Zhang, W. B., Jiang, H. W., Li, C. D., Qiu, P. C., Qi, Z. M., Liu, C. Y., et al. (2010).Integration of QTLs related to soybean cyst nematode resistance based on meta-analysis. Chin. J. Oil Crop Sci. 32, 104–109.

Zhao-Ming, Q., Yanan, S., Lijun, C., Qiang, G., Chunyan, L., Guohua, H., et al.(2009). Meta-analysis of 100-seed weight QTLs in soybean. Scientia AgriculturaSinica 42, 3795–3803.

Zhao-Ming, Q., Ya-Nan, S., Qiong, W., Chun-Yan, L., Guo-Hua, H., and Qing-Shan, C. (2011). A meta-analysis of seed protein concentration QTL in soybean.Can. J. Plant Sci. 91, 221–230. doi: 10.4141/cjps09193

Zhen, Y., Qi, J. L., Wang, S. S., Su, J., Xu, G. H., Zhang, M. S., et al. (2007).Comparative proteome analysis of differentially expressed proteins inducedby Al toxicity in soybean. Physiol. Plant. 131, 542–554. doi: 10.1111/j.1399-3054.2007.00979.x

Zhu, D., Li, Y., Wang, D., Wu, Q., Zhang, D., and Wang, C. (2012). The identifica-tion of single soybean seed variety by laser light backscattering imaging. SensorLett. 10, 1–2. doi: 10.1155/2012/539095

Zhu, J., Patzoldt, W. L., Radwan, O., Tranel, P. J., and Clough, S. J. (2009). Effectsof photosystem-II-interfering herbicides atrazine and bentazon on the soybeantranscriptome. Plant Genome 2, 191–205. doi: 10.3835/plantgenome2009.02.0010

Conflict of Interest Statement: The authors declare that the research was con-ducted in the absence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.

Received: 10 March 2014; accepted: 13 May 2014; published online: 03 June 2014.Citation: Deshmukh R, Sonah H, Patil G, Chen W, Prince S, Mutava R, Vuong T,Valliyodan B and Nguyen HT (2014) Integrating omic approaches for abiotic stresstolerance in soybean. Front. Plant Sci. 5:244. doi: 10.3389/fpls.2014.00244This article was submitted to Plant Genetics and Genomics, a section of the journalFrontiers in Plant Science.Copyright © 2014 Deshmukh, Sonah, Patil, Chen, Prince, Mutava, Vuong,Valliyodan and Nguyen. This is an open-access article distributed under the terms ofthe Creative Commons Attribution License (CC BY). The use, distribution or repro-duction in other forums is permitted, provided the original author(s) or licensor arecredited and that the original publication in this journal is cited, in accordance withaccepted academic practice. No use, distribution or reproduction is permitted whichdoes not comply with these terms.

Frontiers in Plant Science | Plant Genetics and Genomics June 2014 | Volume 5 | Article 244 | 12